from utils import paddle_aux
import os
import paddle
"""
Run inference on images, videos, directories, streams, etc.

Usage - sources:
    $ python path/to/detect.py --weights yolov5s.pt --source 0              # webcam
                                                             img.jpg        # image
                                                             vid.mp4        # video
                                                             path/          # directory
                                                             path/*.jpg     # glob
                                                             'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                                                             'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Usage - formats:
    $ python path/to/detect.py --weights yolov5s.pt                 # PyTorch
                                         yolov5s.torchscript        # TorchScript
                                         yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                         yolov5s.xml                # OpenVINO
                                         yolov5s.engine             # TensorRT
                                         yolov5s.mlmodel            # CoreML (MacOS-only)
                                         yolov5s_saved_model        # TensorFlow SavedModel
                                         yolov5s.pb                 # TensorFlow GraphDef
                                         yolov5s.tflite             # TensorFlow Lite
                                         yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
"""
import argparse
import sys
from pathlib import Path
import cv2
from osgeo import gdal
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))
from models.common import DetectMultiBackend
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from utils.general import LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr, increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, time_sync


def xy2geo(imagepath, xpixel, ypixel):
    dataset = gdal.Open(imagepath)
    GeoTransform = dataset.GetGeoTransform()
    XGeo = GeoTransform[0] + GeoTransform[1] * xpixel + ypixel * GeoTransform[2
        ]
    YGeo = GeoTransform[3] + GeoTransform[4] * xpixel + ypixel * GeoTransform[5
        ]
    return XGeo, YGeo


@paddle.no_grad()
def run(weights=ROOT / 'yolov5s.pt', source=ROOT / 'data/images', data=ROOT /
    'data/coco128.yaml', imgsz=(640, 640), conf_thres=0.25, iou_thres=0.45,
    max_det=1000, device='', view_img=False, save_txt=False, save_conf=
    False, save_crop=False, nosave=False, classes=None, agnostic_nms=False,
    augment=False, visualize=False, update=False, project=ROOT /
    'runs/detect', name='exp', exist_ok=False, line_thickness=3,
    hide_labels=False, hide_conf=False, half=False, dnn=False):
    source = str(source)
    save_img = not nosave and not source.endswith('.txt')
    is_file = Path(source).suffix[1:] in IMG_FORMATS + VID_FORMATS
    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://',
        'https://'))
    webcam = source.isnumeric() or source.endswith('.txt'
        ) or is_url and not is_file
    if is_url and is_file:
        source = check_file(source)
    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True,
        exist_ok=True)
    device = select_device(device)
    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data)
    """Class Attribute: torch.Tensor.names, can not convert, please check whether it is torch.Tensor.*/torch.autograd.function.FunctionCtx.*/torch.distributions.Distribution.* and convert manually"""
    stride, names, pt, jit, onnx, engine = (model.stride, model.names,
        model.pt, model.jit, model.onnx, model.engine)
    imgsz = check_img_size(imgsz, s=stride)
    half &= (pt or jit or onnx or engine) and device.type != 'cpu'
    if pt or jit:
        model.model.half() if half else model.model.float()
    elif engine and model.trt_fp16_input != half:
        LOGGER.info('model ' + ('requires' if model.trt_fp16_input else
            'incompatible with') + ' --half. Adjusting automatically.')
        half = model.trt_fp16_input
    if webcam:
        view_img = check_imshow()
        False = True
        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
        bs = len(dataset)
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
        bs = 1
    vid_path, vid_writer = [None] * bs, [None] * bs
    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz), half=half)
    if not os.path.exists(ROOT / 'input/detection-latlon'):
        os.makedirs(ROOT / 'input/detection-latlon')
    dt, seen = [0.0, 0.0, 0.0], 0
    for path, im, im0s, vid_cap in dataset:
        filename = os.path.basename(path).split('.')[0]
        f1 = open(ROOT / ('input/detection-latlon/' + filename + '.txt'), 'w')
        t1 = time_sync()
        im = paddle.to_tensor(data=im).to(device)
        im = im.astype(dtype='float16') if half else im.astype(dtype='float32')
        im /= 255
        if len(tuple(im.shape)) == 3:
            im = im[None]
        t2 = time_sync()
        dt[0] += t2 - t1
        visualize = increment_path(save_dir / Path(path).stem, mkdir=True
            ) if visualize else False
        pred = model(im, augment=augment, visualize=visualize)
        t3 = time_sync()
        dt[1] += t3 - t2
        pred = non_max_suppression(pred, conf_thres, iou_thres, classes,
            agnostic_nms, max_det=max_det)
        dt[2] += time_sync() - t3
        for i, det in enumerate(pred):
            seen += 1
            if webcam:
                p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(
                    ), dataset.count
            else:
                p, s, im0, frame = path, '', im0s.copy(), getattr(dataset,
                    'frame', 0)
            p = Path(p)
            save_path = str(save_dir / p.name)
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.
                mode == 'image' else f'_{frame}')
            s += '%gx%g ' % tuple(im.shape)[2:]
            gn = paddle.to_tensor(data=tuple(im0.shape))[[1, 0, 1, 0]]
            imc = im0.copy() if save_crop else im0
            annotator = Annotator(im0, line_width=line_thickness, example=
                str(names))
            if len(det):
                det[:, :4] = scale_coords(tuple(im.shape)[2:], det[:, :4],
                    tuple(im0.shape)).round()
                for *xyxy, conf, cls in reversed(det):
                    if save_txt:
                        xywh = (xyxy2xywh(paddle.to_tensor(data=xyxy).view(
                            1, 4)) / gn).view(-1).tolist()
                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh
                            )
                        with open(txt_path + '.txt', 'a') as f:
                            f.write(('%g ' * len(line)).rstrip() % line + '\n')
                        centrex = xywh[0] * tuple(im0s.shape)[1]
                        centrey = xywh[1] * tuple(im0s.shape)[0]
                        xgeo, ygeo = xy2geo(path, centrex, centrey)
                        confidence = conf.item()
                        f1.write('%s %s %4f \n' % (str(xgeo), str(ygeo),
                            confidence))
                    if save_img or save_crop or view_img:
                        c = int(cls)
                        label = None if hide_labels else names[c
                            ] if hide_conf else f'{names[c]} {conf:.2f}'
                        annotator.box_label(xyxy, label, color=colors(c, True))
                        if save_crop:
                            save_one_box(xyxy, imc, file=save_dir / 'crops' /
                                names[c] / f'{p.stem}.jpg', BGR=True)
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "
            im0 = annotator.result()
            if view_img:
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)
            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                else:
                    if vid_path[i] != save_path:
                        vid_path[i] = save_path
                        if isinstance(vid_writer[i], cv2.VideoWriter):
                            vid_writer[i].release()
                        if vid_cap:
                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        else:
                            fps, w, h = 30, tuple(im0.shape)[1], tuple(im0.
                                shape)[0]
                        save_path = str(Path(save_path).with_suffix('.mp4'))
                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.
                            VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                    vid_writer[i].write(im0)
        LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
    t = tuple(x / seen * 1000.0 for x in dt)
    LOGGER.info(
        f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {1, 3, *imgsz}'
         % t)
    if save_txt or save_img:
        s = (
            f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}"
             if save_txt else '')
        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
    if update:
        strip_optimizer(weights)


def parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default=ROOT /
        'runs/train/yolov5s-mobilenetV2improvedV2-jingshan20220814/weights/best.pt'
        , help='model path(s)')
    parser.add_argument('--source', type=str, default=ROOT /
        'data/shangrao/202210/images', help='file/dir/URL/glob, 0 for webcam')
    parser.add_argument('--data', type=str, default=ROOT /
        'data/coco128.yaml', help='(optional) dataset.yaml path')
    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=
        int, default=[1024], help='inference size h,w')
    parser.add_argument('--conf-thres', type=float, default=0.7, help=
        'confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.8, help=
        'NMS IoU threshold')
    parser.add_argument('--max-det', type=int, default=1000, help=
        'maximum detections per image')
    parser.add_argument('--device', default='1', help=
        'cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='show results')
    parser.add_argument('--save-txt', default=True, action='store_true',
        help='save results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help=
        'save confidences in --save-txt labels')
    parser.add_argument('--save-crop', action='store_true', help=
        'save cropped prediction boxes')
    parser.add_argument('--nosave', action='store_true', help=
        'do not save images/videos')
    parser.add_argument('--classes', nargs='+', type=int, help=
        'filter by class: --classes 0, or --classes 0 2 3')
    parser.add_argument('--agnostic-nms', action='store_true', help=
        'class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help=
        'augmented inference')
    parser.add_argument('--visualize', default=False, action='store_true',
        help='visualize features')
    parser.add_argument('--update', action='store_true', help=
        'update all models')
    parser.add_argument('--project', default=ROOT / 'runs/detect', help=
        'save results to project/name')
    parser.add_argument('--name', default='shangrao-202210', help=
        'save results to project/name')
    parser.add_argument('--exist-ok', action='store_true', help=
        'existing project/name ok, do not increment')
    parser.add_argument('--line-thickness', default=3, type=int, help=
        'bounding box thickness (pixels)')
    parser.add_argument('--hide-labels', default=False, action='store_true',
        help='hide labels')
    parser.add_argument('--hide-conf', default=False, action='store_true',
        help='hide confidences')
    parser.add_argument('--half', action='store_true', help=
        'use FP16 half-precision inference')
    parser.add_argument('--dnn', action='store_true', help=
        'use OpenCV DNN for ONNX inference')
    opt = parser.parse_args()
    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1
    print_args(FILE.stem, opt)
    return opt


def main(opt):
    check_requirements(exclude=('tensorboard', 'thop'))
    run(**vars(opt))


if __name__ == '__main__':
    opt = parse_opt()
    main(opt)
